Factory vs Devin: Autonomous Software Engineering, Compared

The “AI software engineer” label gets slapped on a wide range of products — from autocomplete-on-steroids to fully autonomous agents that file pull requests while you sleep. Factory and Devin sit at the serious end of that spectrum, but they’ve made meaningfully different bets on what autonomous engineering actually looks like in practice. Here’s how they compare across the dimensions that matter: what they’ll take off your plate, how much they expect engineers to stay in the loop, what the benchmarks actually tell you, and what you’ll pay.

What Each Product Is Actually Doing

Devin is Cognition AI’s autonomous software engineering agent that operates independently in a sandboxed cloud environment with its own IDE, shell, and browser. Given a task via Slack, Linear, Jira, or direct input, Devin reads the codebase, generates an Interactive Planning blueprint with a confidence score, writes code across multiple files, runs and debugs tests, and creates pull requests. The product philosophy is maximum autonomy: hand off a well-scoped task and expect output, not a conversation.

Factory is an agent-native software development platform built around coding agents called Droids. It is designed to help engineering teams delegate software development tasks to AI agents that can plan, execute, review, and ship work across complex codebases. But Factory’s angle is distinctly team-oriented. Factory positions itself as infrastructure-agnostic: Droids work across any IDE, CLI, Slack, Linear, or web interface. Where Devin optimizes for a single autonomous agent completing a task end-to-end, Factory’s framing leans on the term “Async” — non-blocking but explicitly allowing human collaboration, rather than pure fire-and-forget.

Factory builds specialized AI agents designed to handle different aspects of the software development lifecycle including coding, knowledge retrieval, and reliability/incident response. Their focus is specifically on enterprise use cases, serving Fortune 500 companies with complex, legacy codebases that may be 30+ years old. That’s a different customer than Devin’s individual-developer and mid-size-team target.

Task Scope and What You Can Delegate

Devin’s sweet spot is well-defined, long-horizon tasks. Devin stands out in tasks such as language migrations, CI/CD automation, ETL development, and SaaS integrations, adapting to unique workflows and codebases.

Devin maintains context across long-running tasks and learns from interactions over time — when working on a multi-file refactoring, it recalls relevant context at every step rather than losing track of earlier decisions, and incorporates corrections when developers provide feedback.

Factory’s Droids cover more surface area across the SDLC as a system. The docs position Factory as a platform that works across the places developers already work: CLI, desktop, SDK, headless execution, code review, automated QA, GitHub, Linear, Slack, IDEs, and enterprise environments.

When a CI/CD pipeline fails, Factory Droids automatically intercept the error, diagnose the root cause, write a patch, and re-submit the Pull Request for human approval. Factory is also investing in what it calls Missions — larger structured work items aimed at multi-step project execution.

The practical distinction: Devin is a single very capable async engineer you hand tickets to. Factory is closer to an agent-native engineering infrastructure layer that wires into your existing toolchain and processes at the org level.

Human-in-the-Loop Design

This is where the philosophies diverge most sharply. Devin’s design presupposes that senior engineer oversight is still required in the loop for anything non-trivial. Senior engineer oversight remains required for complex or novel tasks.

The single biggest factor in Devin’s success is the quality of the task description — operators consistently report that vague inputs produce wasted ACUs, not usable code.

Users can interact with Devin 2.0 through a VSCode-inspired interface that allows for reviewing and editing Devin’s work, as well as running tests directly within the platform’s environment. This flexibility supports both hands-on and hands-off workflows, depending on user preference.

Factory bakes review gates into its architecture more explicitly. By integrating its cloud tools and local development environment, Factory maintains the team’s unique workflows and standards, allowing tasks to be executed autonomously or in collaboration.

Teams and Enterprise add controls such as SSO/SAML/SCIM, ZDR, model selection, autonomy controls, org-level deny lists, audit logging, data residency, session retention, and network policy — a governance surface that signals enterprise buyers with compliance requirements. The autonomy dial is explicit, not assumed.

For operators building /tools/claude-code/-style terminal workflows alongside an agent layer, Factory’s model gives you finer control. Devin’s model requires more discipline on your side: write good specs or accept mediocre output.

The Benchmark Question

Both products have complicated relationships with benchmarks, and you should read them carefully before quoting them in a vendor evaluation.

On the industry-standard SWE-bench benchmark, Devin resolves 13.86% of real GitHub issues end-to-end — a 7x improvement over previous AI models at 1.96%, though independent testing shows 15–30% success rates in practice.

That SWE-bench score was a landmark when announced in 2024, but has since been surpassed by Claude, GPT-4o, and Gemini models on the same benchmark. Cognition’s own response to this is notable: the company argued that “performance on coding benchmarks is often not representative of the real-world experience of using an agent, which is why we stopped reporting SWE-Bench numbers in 2024.”

Cognition has since shifted focus to its own SWE model family. On Scale AI’s SWE-Bench Pro benchmark, SWE-1.5 scored 40.08%, ranking second after Claude Sonnet 4.5’s 43.60%.

Factory has taken a different benchmarking posture. Droid claimed the #1 position on Terminal-Bench at 58.75% with Claude Opus 4.1, arguing harness design matters as much as model choice. That’s a reasonable argument — and a harder one to independently verify. Both companies are doing what all AI companies do: cherry-pick the benchmark that makes them look best. Discount both sets of numbers by 30% and evaluate on tasks that resemble your actual backlog.

If you’re comparing coding tools more broadly, see our cursor vs. claude code and claude code vs. devin breakdowns for additional signal.

Pricing

Devin’s pricing has changed dramatically since launch. Devin 2.0 (April 2025) reduced entry pricing from $500 to $20/month on a pay-as-you-go ACU model at $2.25/ACU and introduced parallel Devin agents for concurrent task execution.

The Team Plan at $500/month targets medium-sized companies needing an AI coding assistant for multiple projects, providing 250 Agent Compute Units, with additional ACUs at $2 each.

Enterprise pricing is custom with VPC deployment and SAML SSO. The key cost risk: total monthly cost scales with ACU consumption, which is unpredictable for complex tasks.

Factory’s pricing is structured differently. Pro is $20/month, Plus is $100/month, Max is $200/month, with Teams and Enterprise tiers adding ZDR, SSO, and governance controls. Factory’s per-seat model is more predictable than Devin’s consumption-based ACUs — a meaningful operational consideration for teams with variable workloads.

For teams already running /tools/cursor/ or /tools/github-copilot/ for inline assistance, both Factory and Devin are additive — they handle async, multi-step tasks that IDE-integrated tools don’t touch. The question is whether you want a single autonomous agent (Devin) or a platform that wires agents into your whole engineering org (Factory).

Bottom Line

Devin is the right bet if your team has well-defined ticket work — migrations, integrations, CI/CD automation — and the engineering discipline to write specifications that set an agent up to succeed. Factory is the right bet if you want agent infrastructure wired into your entire engineering stack, with governance controls that survive a security audit at an enterprise buyer. Neither product should be evaluated on benchmark scores alone; the real test is how they handle the messy, ambiguous tasks sitting at the bottom of your backlog right now.